Lengthy download times experienced when using a mobile network, compared with using a fixed DSL network, for example, are known to reduce a user's quality of experience (QoE). The user, when comparing the mobile network performance with a fixed broadband network performance, will often observe that the download performance is slower over the mobile network. FIG. 1 plots the measured performance for loading a web page over a fixed DSL network, and illustrates the dependency of download speed with bandwidth limit and latency. It can be seen that networks with a higher latency, a feature common in mobile networks, do not efficiently utilize connections with ever increasing bandwidth.
One proposal to address this problem is to predict the required data content, and to push the required data content to a client before the client requests it. Using the same evaluation as in FIG. 1, FIG. 2 highlights the advantage of using such a method. In the context of the client being a web browser to access an Internet website, prediction may be based on parameters such as the user's browsing history, or information obtained from the browsing habits of other users who have accessed that web site.
A problem with using a predictive method stems from the dynamic nature of modern web content. The predictive technique works reasonably well with a static webs site. However, an increasing number of web sites feature dynamic content. For example, a web portal may draw in information from many different web sites, and update this information at differing time intervals depending on the source of the information. A weather application displayed on the web portal may be updated hourly, for example, whereas a breaking news section may be updated every few minutes. Furthermore, the rate and/or time at which such data is updated may depend on the type of web browser the user is using, or any number of parameters relating to the network terminal on which the web browser is running. Furthermore, information requested by the web browser may be generated randomly, as can sometimes be the case when displaying advertisements on web pages. Unlike static web content, dynamic information as described cannot simply be cached and/or pushed to a client because the required information cannot be easily predicted. Furthermore, as web page sophistication improves, the proportional split between dynamic and static web content is likely to increase.
Some specific web portals may be designed to offer dynamical information before it is requested by the client, but this would require an optimised design and so is not very common. It would also only be available on that specific web portal, and so does nothing to improve the mobile browser situation generally.
A simple solution that a network operator could employ, in order to enhance the QoE for a user, is to increase the bandwidth available to the user. Practically, this is a costly exercise, and as can be seen in FIG. 1, would still result in high latency network connections that do not make efficient use of the improved bandwidth.
Increasing the cache size of a network in order to cache web pages is a further solution to enhance the quality of experience for the user, but such caching techniques only improve the performance for loading static objects.
There has been a general shift towards processing data in the Cloud at data-centres. These data-centres possess vast CPU and memory resources. An example utilising this type of model is the Amazon Silk browser, in which some of the processing is done at a network terminal, and the rest is done at an Amazon data-centre. Users accessing the internet in this way become dependant on a given data-centre, which may be physically located a great distance from the user. This can further reduce the QoE for the user.